Abstract: In recent years,the rapid development of large language models has garnered widespread attention across various sectors.While these models naturally excel at various natural language processing tasks,their performance in domain-specific question answering tasks often falls short due to a lack of specialized training in vertical domains,leading to unreliable and less applicable answers.To improve the performance of domain knowledge question answering systems,this paper proposes a novel approach based on compositional context prompting for large language models.Compositional context prompting consists of domain knowledge context and question-answer example context.The domain knowledge context is retrieved from the domain knowledge base using a contrastive learning based dense retriever,which can enhance the domain expertise processing ability of large language models.The question-answer example context is obtained through semantic similarity retrieval from the training set,which improves the large language model's understanding of question intent.Finally,the obtained composite context prompts are inputted into the large-scale language model fine-tuned with domain knowledge to generate the final domain answers.Through extensive experiments and comprehensive comparisons with baseline models,the proposed method achieves an improvement of 15.91% in precision and 16.14% in recall on the BERTScore metric compared to ChatGPT,with an F1 Score improvement of 15.87%.

Key words: Large language models, Domain knowledge question-answering, Compositional context prompting, Contrastive lear-ning, Retrieval